Quantitative Inpainting Results.
In Tab. REF , we utilize PSNR, SSIM [1]}, FID [2]}, and LPIPS [3]} to assess the performance of all compared methods blackincluding our conference version on the Indoor and Places2 datasets in 256\(\times \) 256 with mixed segmentation and irregular masks.
More results with different masking rates are shown in the supplementary.
For Indoor, our blackZITS++ and ZITS achieve the best and the second best results on all metrics. MST can get slightly better results compared with EC, which is benefited by the usage of lines.
LaMa can get more acceptable FID and LPIPS while our ZITS can achieve significant improvements based on LaMa due to the seamlessly embedded structural information and positional encoding.
Note that the gap between ZITS++ and MST is also caused by the different quality of recovered structures as discussed below.
For Places2, HiFill fails to get good results with large masks, which may be caused by its limited model capacity. Note that Co-Mod has a low FID and LPIPS on Places2. However, Co-Mod is trained with a sophisticated StyleGAN [4]} with much more training data compared with others. And our ZITS can even achieve better results compared with Co-Mod with limited data scale and training steps. blackMoreover, ZITS++ improved in this paper can even outperform ZITS. In general, our method has superior performance compared with LaMa, which is valuable with limited finetune steps. And LaMa in Tab. REF  are trained with the same total steps as ZITS++.
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